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train_reward_model.py
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train_reward_model.py
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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch.multiprocessing as mp
from omegaconf.omegaconf import OmegaConf
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
from nemo_aligner.algorithms.supervised import SupervisedTrainer
from nemo_aligner.data.nlp.builders import (
build_dataloader,
build_train_valid_test_regression_rm_datasets,
build_train_valid_test_rm_datasets,
)
from nemo_aligner.models.nlp.gpt.reward_model_classes import REWARD_MODEL_CLASS_DICT, RewardModelType
from nemo_aligner.utils.distributed import Timer
from nemo_aligner.utils.train_script_utils import (
CustomLoggerWrapper,
add_custom_checkpoint_callback,
extract_optimizer_scheduler_from_ptl_model,
init_distributed,
init_using_ptl,
resolve_and_create_trainer,
retrieve_custom_trainer_state_dict,
)
from nemo_aligner.utils.utils import load_and_override_model_config, load_from_nemo
"""Script to start Reward Model training"""
OmegaConf.register_new_resolver("multiply", lambda x, y: x * y, replace=True)
OmegaConf.register_new_resolver("int_div", lambda x, y: x // y, replace=True)
mp.set_start_method("spawn", force=True)
@hydra_runner(config_path="conf", config_name="training_rm")
def main(cfg) -> None:
"""
Binary ranking reward models use comparison based objective similar to the one found in the
InstructGPT paper: https://arxiv.org/pdf/2203.02155.pdf and have no explicit labels.
Regression reward models use a MSE loss to fit multi-attribute numeric labels for each data point.
"""
reward_model_type = RewardModelType(cfg.model.get("reward_model_type", "binary_ranking"))
reward_model_cls = REWARD_MODEL_CLASS_DICT[reward_model_type]
cfg.model = load_and_override_model_config(cfg.pretrained_checkpoint.restore_from_path, cfg.model)
logging.info("\n\n************** Experiment configuration ***********")
logging.info(f"\n{OmegaConf.to_yaml(cfg)}")
trainer = resolve_and_create_trainer(cfg, "rm")
exp_manager(trainer, cfg.exp_manager)
logger = CustomLoggerWrapper(trainer.loggers)
ptl_model = load_from_nemo(
reward_model_cls,
cfg.model,
trainer,
strict=True,
load_base_model_only=True,
restore_path=cfg.pretrained_checkpoint.restore_from_path,
)
# pull values from checkpoint
trainer_restore_path = trainer.ckpt_path
if trainer_restore_path is not None:
custom_trainer_state_dict = retrieve_custom_trainer_state_dict(trainer)
consumed_samples = custom_trainer_state_dict["consumed_samples"]
else:
custom_trainer_state_dict = None
consumed_samples = 0
init_distributed(trainer, ptl_model, cfg.model.get("transformer_engine", False))
# use the entire dataset
train_valid_test_num_samples = [-1 * cfg.model.global_batch_size] * 3
if reward_model_type == RewardModelType.BINARY_RANKING:
dataset_builder = build_train_valid_test_rm_datasets
elif reward_model_type == RewardModelType.REGRESSION:
dataset_builder = build_train_valid_test_regression_rm_datasets
else:
raise ValueError(f"Only support binary_ranking and regression reward model, but get {reward_model_type} ")
train_ds, validation_ds, _ = dataset_builder(
cfg=cfg.model,
data_prefix=cfg.model.data.data_prefix,
data_impl=cfg.model.data.data_impl,
splits_string=cfg.model.data.splits_string,
train_valid_test_num_samples=train_valid_test_num_samples,
seq_length=cfg.model.data.seq_length,
seed=cfg.model.seed,
tokenizer=ptl_model.tokenizer,
)
train_dataloader = build_dataloader(
cfg=cfg,
dataset=train_ds,
consumed_samples=consumed_samples,
mbs=cfg.model.micro_batch_size,
gbs=cfg.model.global_batch_size,
load_gbs=True,
)
val_dataloader = build_dataloader(
cfg=cfg,
dataset=validation_ds,
consumed_samples=0,
mbs=cfg.model.micro_batch_size,
gbs=cfg.model.global_batch_size,
load_gbs=True,
use_random_sampler=False,
)
init_using_ptl(trainer, ptl_model, train_dataloader, train_ds)
optimizer, scheduler = extract_optimizer_scheduler_from_ptl_model(ptl_model)
ckpt_callback = add_custom_checkpoint_callback(trainer, ptl_model)
logger.log_hyperparams(OmegaConf.to_container(cfg))
timer = Timer(cfg.exp_manager.get("max_time_per_run"))
rm_trainer = SupervisedTrainer(
cfg=cfg.trainer.rm,
model=ptl_model,
optimizer=optimizer,
scheduler=scheduler,
train_dataloader=train_dataloader,
val_dataloader=val_dataloader,
test_dataloader=None,
logger=logger,
ckpt_callback=ckpt_callback,
run_timer=timer,
)
if custom_trainer_state_dict is not None:
rm_trainer.load_state_dict(custom_trainer_state_dict)
rm_trainer.fit()
if __name__ == "__main__":
main()